A Sustainable W-RLG Model for Attack Detection in Healthcare IoT Systems

Brij B. Gupta (), Akshat Gaurav, Razaz Waheeb Attar, Varsha Arya, Ahmed Alhomoud and Kwok Tai Chui
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Brij B. Gupta: Department of Computer Science and Information Engineering, Asia University, Taichung 413, Taiwan
Akshat Gaurav: Computer Science and Engineering, Ronin Institute, Montclair, NJ 07043, USA
Razaz Waheeb Attar: Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Varsha Arya: Department of Business Administration, Asia University, Taichung City 41354, Taiwan
Ahmed Alhomoud: Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
Kwok Tai Chui: Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University (HKMU), Hong Kong

Sustainability, 2024, vol. 16, issue 8, 1-15

Abstract: The increasingly widespread use of IoT devices in healthcare systems has heightened the need for sustainable and efficient cybersecurity measures. In this paper, we introduce the W-RLG Model, a novel deep learning approach that combines Whale Optimization with Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) for attack detection in healthcare IoT systems. Leveraging the strengths of these algorithms, the W-RLG Model identifies potential cyber threats with remarkable accuracy, protecting the integrity and privacy of sensitive health data. This model’s precision, recall, and F1-score are unparalleled, being significantly better than those achieved using traditional machine learning methods, and its sustainable design addresses the growing concerns regarding computational resource efficiency, making it a pioneering solution for shielding digital health ecosystems from evolving cyber threats.

Keywords: sustainable cybersecurity; healthcare IoT systems; whale optimization algorithm; deep learning models; attack detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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